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            <article class="content wrap" id="_content" data-uid="Keras.Callbacks.TensorBoard">
  
  
  <h1 id="Keras_Callbacks_TensorBoard" data-uid="Keras.Callbacks.TensorBoard" class="text-break">Class TensorBoard
  </h1>
  <div class="markdown level0 summary"><p>TensorBoard basic visualizations. TensorBoard is a visualization tool provided with TensorFlow.
This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.
If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line:    tensorboard --logdir=/ full_path_to_your_logs
When using a backend other than TensorFlow, TensorBoard will still work(if you have TensorFlow installed), but the only feature available will be the display of the losses and metrics plots.</p>
</div>
  <div class="markdown level0 conceptual"></div>
  <div class="inheritance">
    <h5>Inheritance</h5>
    <div class="level0"><span class="xref">System.Object</span></div>
    <div class="level1"><a class="xref" href="Keras.Keras.html">Keras</a></div>
    <div class="level2"><a class="xref" href="Keras.Base.html">Base</a></div>
    <div class="level3"><a class="xref" href="Keras.Callbacks.Callback.html">Callback</a></div>
    <div class="level4"><span class="xref">TensorBoard</span></div>
  </div>
  <div classs="implements">
    <h5>Implements</h5>
    <div><span class="xref">System.IDisposable</span></div>
  </div>
  <div class="inheritedMembers">
    <h5>Inherited Members</h5>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_Parameters">Base.Parameters</a>
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    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_None">Base.None</a>
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    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_Init">Base.Init()</a>
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    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_ToPython">Base.ToPython()</a>
    </div>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_InvokeStaticMethod_System_Object_System_String_System_Collections_Generic_Dictionary_System_String_System_Object__">Base.InvokeStaticMethod(Object, String, Dictionary&lt;String, Object&gt;)</a>
    </div>
    <div>
      <a class="xref" href="Keras.Base.html#Keras_Base_InvokeMethod_System_String_System_Collections_Generic_Dictionary_System_String_System_Object__">Base.InvokeMethod(String, Dictionary&lt;String, Object&gt;)</a>
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      <a class="xref" href="Keras.Base.html#Keras_Base_Item_System_String_">Base.Item[String]</a>
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    <div>
      <a class="xref" href="Keras.Keras.html#Keras_Keras_Instance">Keras.Instance</a>
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    <div>
      <a class="xref" href="Keras.Keras.html#Keras_Keras_keras">Keras.keras</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_keras2onnx">Keras.keras2onnx</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_tfjs">Keras.tfjs</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_Dispose">Keras.Dispose()</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_ToTuple_System_Array_">Keras.ToTuple(Array)</a>
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      <a class="xref" href="Keras.Keras.html#Keras_Keras_ToList_System_Array_">Keras.ToList(Array)</a>
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    <div>
      <span class="xref">System.Object.Equals(System.Object)</span>
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      <span class="xref">System.Object.Equals(System.Object, System.Object)</span>
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      <span class="xref">System.Object.GetHashCode()</span>
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      <span class="xref">System.Object.GetType()</span>
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      <span class="xref">System.Object.MemberwiseClone()</span>
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      <span class="xref">System.Object.ReferenceEquals(System.Object, System.Object)</span>
    </div>
    <div>
      <span class="xref">System.Object.ToString()</span>
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  </div>
  <h6><strong>Namespace</strong>: <a class="xref" href="Keras.Callbacks.html">Keras.Callbacks</a></h6>
  <h6><strong>Assembly</strong>: Keras.dll</h6>
  <h5 id="Keras_Callbacks_TensorBoard_syntax">Syntax</h5>
  <div class="codewrapper">
    <pre><code class="lang-csharp hljs">public class TensorBoard : Callback, IDisposable</code></pre>
  </div>
  <h3 id="constructors">Constructors
  </h3>
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  <span class="small pull-right mobile-hide">
    <a href="https://github.com/SciSharp/Keras.NET/blob/master/Keras/Callbacks.cs/#L255">View Source</a>
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  <a id="Keras_Callbacks_TensorBoard__ctor_" data-uid="Keras.Callbacks.TensorBoard.#ctor*"></a>
  <h4 id="Keras_Callbacks_TensorBoard__ctor_System_String_System_Int32_System_Int32_System_Boolean_System_Boolean_System_Boolean_System_Int32_System_String___System_Collections_Generic_Dictionary_System_String_System_String__Numpy_NDarray_System_String_" data-uid="Keras.Callbacks.TensorBoard.#ctor(System.String,System.Int32,System.Int32,System.Boolean,System.Boolean,System.Boolean,System.Int32,System.String[],System.Collections.Generic.Dictionary{System.String,System.String},Numpy.NDarray,System.String)">TensorBoard(String, Int32, Int32, Boolean, Boolean, Boolean, Int32, String[], Dictionary&lt;String, String&gt;, NDarray, String)</h4>
  <div class="markdown level1 summary"><p>Initializes a new instance of the <a class="xref" href="Keras.Callbacks.LearningRateScheduler.html">LearningRateScheduler</a> class.</p>
</div>
  <div class="markdown level1 conceptual"></div>
  <h5 class="decalaration">Declaration</h5>
  <div class="codewrapper">
    <pre><code class="lang-csharp hljs">public TensorBoard(string log_dir = &quot;./logs&quot;, int histogram_freq = 0, int batch_size = 32, bool write_graph = true, bool write_grads = false, bool write_images = false, int embeddings_freq = 0, string[] embeddings_layer_names = null, Dictionary&lt;string, string&gt; embeddings_metadata = null, NDarray embeddings_data = null, string update_freq = &quot;epoch&quot;)</code></pre>
  </div>
  <h5 class="parameters">Parameters</h5>
  <table class="table table-bordered table-striped table-condensed">
    <thead>
      <tr>
        <th>Type</th>
        <th>Name</th>
        <th>Description</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td><span class="xref">System.String</span></td>
        <td><span class="parametername">log_dir</span></td>
        <td><p>the path of the directory where to save the log files to be parsed by TensorBoard.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Int32</span></td>
        <td><span class="parametername">histogram_freq</span></td>
        <td><p>frequency (in epochs) at which to compute activation and weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Int32</span></td>
        <td><span class="parametername">batch_size</span></td>
        <td><p>size of batch of inputs to feed to the network for histograms computation.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Boolean</span></td>
        <td><span class="parametername">write_graph</span></td>
        <td><p>whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Boolean</span></td>
        <td><span class="parametername">write_grads</span></td>
        <td><p>whether to visualize gradient histograms in TensorBoard. histogram_freq must be greater than 0.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Boolean</span></td>
        <td><span class="parametername">write_images</span></td>
        <td><p>whether to write model weights to visualize as image in TensorBoard.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Int32</span></td>
        <td><span class="parametername">embeddings_freq</span></td>
        <td><p>frequency (in epochs) at which selected embedding layers will be saved. If set to 0, embeddings won't be computed. Data to be visualized in TensorBoard's Embedding tab must be passed as embeddings_data.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.String</span>[]</td>
        <td><span class="parametername">embeddings_layer_names</span></td>
        <td><p>a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.Collections.Generic.Dictionary</span>&lt;<span class="xref">System.String</span>, <span class="xref">System.String</span>&gt;</td>
        <td><span class="parametername">embeddings_metadata</span></td>
        <td><p>a dictionary which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">Numpy.NDarray</span></td>
        <td><span class="parametername">embeddings_data</span></td>
        <td><p>data to be embedded at layers specified in embeddings_layer_names. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn more about embeddings.</p>
</td>
      </tr>
      <tr>
        <td><span class="xref">System.String</span></td>
        <td><span class="parametername">update_freq</span></td>
        <td></td>
      </tr>
    </tbody>
  </table>
  <h3 id="implements">Implements</h3>
  <div>
      <span class="xref">System.IDisposable</span>
  </div>
</article>
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